Labb4_RNN_ModelC1only.ipynb

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   "source": [
    "\n",
    "###### Lab 4 - RNN - Inl B\n",
    "<br>\n",
    "umu-id: anth2786<br>\n",
    "<br>\n",
    "The first part (the data gathering), the code is equivalent to the code in the tutorial at:<br>\n",
    "&nbsp;&nbsp;&nbsp;    https://www.tensorflow.org/tutorials/structured_data/time_series <br>\n",
    "It runs from Setup to \"The lab\". The code from that tutorial is under Apache 2.0 licensing. More info on the licensing is in the end of this notebook.\n",
    "<br><br>\n",
    "\n",
    "For the lab part, part of the code is based on the same tutorial, but adapted. Others are based on code from the previous labs:<br>\n",
    "&nbsp;&nbsp;&nbsp;    https://github.com/dmfow/dl1<br>\n",
    "&nbsp;&nbsp;&nbsp;    https://github.com/dmfow/dl2<br>\n",
    "&nbsp;&nbsp;&nbsp;    https://github.com/dmfow/dl3<br><br>\n",
    "and from the webpage:<br>\n",
    "&nbsp;&nbsp;&nbsp;    https://keras.io/api/layers/recurrent_layers/lstm/<br>\n",
    "&nbsp;&nbsp;&nbsp;    https://keras.io/api/layers/recurrent_layers/gru/<br>\n",
    "\n",
    "<br>\n",
    "<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "\n",
    "# Disable running on GPU\n",
    "os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"-1\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "XVhK72Pu1cJL"
   },
   "source": [
    "## Setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "execution": {
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   "source": [
    "import datetime\n",
    "\n",
    "import IPython\n",
    "import IPython.display\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import tensorflow as tf\n",
    "\n",
    "mpl.rcParams['figure.figsize'] = (8, 6)\n",
    "mpl.rcParams['axes.grid'] = False"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "TokBlnUhWFw9"
   },
   "source": [
    "## The weather dataset\n",
    "This tutorial uses a <a href=\"https://www.bgc-jena.mpg.de/wetter/\" class=\"external\">weather time series dataset</a> recorded by the <a href=\"https://www.bgc-jena.mpg.de\" class=\"external\">Max Planck Institute for Biogeochemistry</a>.\n",
    "\n",
    "This dataset contains 14 different features such as air temperature, atmospheric pressure, and humidity. These were collected every 10 minutes, beginning in 2003. For efficiency, you will use only the data collected between 2009 and 2016. This section of the dataset was prepared by François Chollet for his book [Deep Learning with Python](https://www.manning.com/books/deep-learning-with-python)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-15T01:30:14.222400Z",
     "iopub.status.busy": "2020-10-15T01:30:14.220813Z",
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     "shell.execute_reply": "2020-10-15T01:30:14.855425Z"
    },
    "id": "xyv_i85IWInT"
   },
   "outputs": [],
   "source": [
    "zip_path = tf.keras.utils.get_file(\n",
    "    origin='https://storage.googleapis.com/tensorflow/tf-keras-datasets/jena_climate_2009_2016.csv.zip',\n",
    "    fname='jena_climate_2009_2016.csv.zip',\n",
    "    extract=True)\n",
    "csv_path, _ = os.path.splitext(zip_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "R81Wx8WP4c3G"
   },
   "source": [
    "This tutorial will just deal with **hourly predictions**, so start by sub-sampling the data from 10 minute intervals to 1h:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-15T01:30:14.860568Z",
     "iopub.status.busy": "2020-10-15T01:30:14.859771Z",
     "iopub.status.idle": "2020-10-15T01:30:15.702562Z",
     "shell.execute_reply": "2020-10-15T01:30:15.701846Z"
    },
    "id": "TX6uGeeeWIkG"
   },
   "outputs": [],
   "source": [
    "df = pd.read_csv(csv_path)\n",
    "# slice [start:stop:step], starting from index 5 take every 6th record.\n",
    "df = df[5::6]\n",
    "\n",
    "date_time = pd.to_datetime(df.pop('Date Time'), format='%d.%m.%Y %H:%M:%S')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "VdbOWXiTWM2T"
   },
   "source": [
    "Let's take a glance at the data. Here are the first few rows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-15T01:30:15.718067Z",
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     "shell.execute_reply": "2020-10-15T01:30:15.727725Z"
    },
    "id": "ojHE-iCCWIhz"
   },
   "outputs": [
    {
     "data": {
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       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>p (mbar)</th>\n",
       "      <th>T (degC)</th>\n",
       "      <th>Tpot (K)</th>\n",
       "      <th>Tdew (degC)</th>\n",
       "      <th>rh (%)</th>\n",
       "      <th>VPmax (mbar)</th>\n",
       "      <th>VPact (mbar)</th>\n",
       "      <th>VPdef (mbar)</th>\n",
       "      <th>sh (g/kg)</th>\n",
       "      <th>H2OC (mmol/mol)</th>\n",
       "      <th>rho (g/m**3)</th>\n",
       "      <th>wv (m/s)</th>\n",
       "      <th>max. wv (m/s)</th>\n",
       "      <th>wd (deg)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>996.50</td>\n",
       "      <td>-8.05</td>\n",
       "      <td>265.38</td>\n",
       "      <td>-8.78</td>\n",
       "      <td>94.4</td>\n",
       "      <td>3.33</td>\n",
       "      <td>3.14</td>\n",
       "      <td>0.19</td>\n",
       "      <td>1.96</td>\n",
       "      <td>3.15</td>\n",
       "      <td>1307.86</td>\n",
       "      <td>0.21</td>\n",
       "      <td>0.63</td>\n",
       "      <td>192.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>996.62</td>\n",
       "      <td>-8.88</td>\n",
       "      <td>264.54</td>\n",
       "      <td>-9.77</td>\n",
       "      <td>93.2</td>\n",
       "      <td>3.12</td>\n",
       "      <td>2.90</td>\n",
       "      <td>0.21</td>\n",
       "      <td>1.81</td>\n",
       "      <td>2.91</td>\n",
       "      <td>1312.25</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.63</td>\n",
       "      <td>190.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>996.84</td>\n",
       "      <td>-8.81</td>\n",
       "      <td>264.59</td>\n",
       "      <td>-9.66</td>\n",
       "      <td>93.5</td>\n",
       "      <td>3.13</td>\n",
       "      <td>2.93</td>\n",
       "      <td>0.20</td>\n",
       "      <td>1.83</td>\n",
       "      <td>2.94</td>\n",
       "      <td>1312.18</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.63</td>\n",
       "      <td>167.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>996.99</td>\n",
       "      <td>-9.05</td>\n",
       "      <td>264.34</td>\n",
       "      <td>-10.02</td>\n",
       "      <td>92.6</td>\n",
       "      <td>3.07</td>\n",
       "      <td>2.85</td>\n",
       "      <td>0.23</td>\n",
       "      <td>1.78</td>\n",
       "      <td>2.85</td>\n",
       "      <td>1313.61</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.38</td>\n",
       "      <td>240.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>997.46</td>\n",
       "      <td>-9.63</td>\n",
       "      <td>263.72</td>\n",
       "      <td>-10.65</td>\n",
       "      <td>92.2</td>\n",
       "      <td>2.94</td>\n",
       "      <td>2.71</td>\n",
       "      <td>0.23</td>\n",
       "      <td>1.69</td>\n",
       "      <td>2.71</td>\n",
       "      <td>1317.19</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.88</td>\n",
       "      <td>157.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    p (mbar)  T (degC)  Tpot (K)  Tdew (degC)  rh (%)  VPmax (mbar)  \\\n",
       "5     996.50     -8.05    265.38        -8.78    94.4          3.33   \n",
       "11    996.62     -8.88    264.54        -9.77    93.2          3.12   \n",
       "17    996.84     -8.81    264.59        -9.66    93.5          3.13   \n",
       "23    996.99     -9.05    264.34       -10.02    92.6          3.07   \n",
       "29    997.46     -9.63    263.72       -10.65    92.2          2.94   \n",
       "\n",
       "    VPact (mbar)  VPdef (mbar)  sh (g/kg)  H2OC (mmol/mol)  rho (g/m**3)  \\\n",
       "5           3.14          0.19       1.96             3.15       1307.86   \n",
       "11          2.90          0.21       1.81             2.91       1312.25   \n",
       "17          2.93          0.20       1.83             2.94       1312.18   \n",
       "23          2.85          0.23       1.78             2.85       1313.61   \n",
       "29          2.71          0.23       1.69             2.71       1317.19   \n",
       "\n",
       "    wv (m/s)  max. wv (m/s)  wd (deg)  \n",
       "5       0.21           0.63     192.7  \n",
       "11      0.25           0.63     190.3  \n",
       "17      0.18           0.63     167.2  \n",
       "23      0.10           0.38     240.0  \n",
       "29      0.40           0.88     157.0  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "WRzj1inMfgcO"
   },
   "source": [
    "Here is the evolution of a few features over time. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-15T01:30:15.735643Z",
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     "shell.execute_reply": "2020-10-15T01:30:17.213289Z"
    },
    "id": "Vg5XIc5tfNlG"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_cols = ['T (degC)', 'p (mbar)', 'rho (g/m**3)']\n",
    "plot_features = df[plot_cols]\n",
    "plot_features.index = date_time\n",
    "_ = plot_features.plot(subplots=True)\n",
    "\n",
    "plot_features = df[plot_cols][:480]\n",
    "plot_features.index = date_time[:480]\n",
    "_ = plot_features.plot(subplots=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "wXWLG0_WBhZS"
   },
   "source": [
    "### Inspect and cleanup"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "yhmZXJew6GlS"
   },
   "source": [
    "Next look at the statistics of the dataset:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-15T01:30:17.226668Z",
     "iopub.status.busy": "2020-10-15T01:30:17.225942Z",
     "iopub.status.idle": "2020-10-15T01:30:17.302409Z",
     "shell.execute_reply": "2020-10-15T01:30:17.302876Z"
    },
    "id": "h510pgKVrrai"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>p (mbar)</th>\n",
       "      <td>70091.0</td>\n",
       "      <td>989.212842</td>\n",
       "      <td>8.358886</td>\n",
       "      <td>913.60</td>\n",
       "      <td>984.20</td>\n",
       "      <td>989.57</td>\n",
       "      <td>994.720</td>\n",
       "      <td>1015.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T (degC)</th>\n",
       "      <td>70091.0</td>\n",
       "      <td>9.450482</td>\n",
       "      <td>8.423384</td>\n",
       "      <td>-22.76</td>\n",
       "      <td>3.35</td>\n",
       "      <td>9.41</td>\n",
       "      <td>15.480</td>\n",
       "      <td>37.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tpot (K)</th>\n",
       "      <td>70091.0</td>\n",
       "      <td>283.493086</td>\n",
       "      <td>8.504424</td>\n",
       "      <td>250.85</td>\n",
       "      <td>277.44</td>\n",
       "      <td>283.46</td>\n",
       "      <td>289.530</td>\n",
       "      <td>311.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tdew (degC)</th>\n",
       "      <td>70091.0</td>\n",
       "      <td>4.956471</td>\n",
       "      <td>6.730081</td>\n",
       "      <td>-24.80</td>\n",
       "      <td>0.24</td>\n",
       "      <td>5.21</td>\n",
       "      <td>10.080</td>\n",
       "      <td>23.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rh (%)</th>\n",
       "      <td>70091.0</td>\n",
       "      <td>76.009788</td>\n",
       "      <td>16.474920</td>\n",
       "      <td>13.88</td>\n",
       "      <td>65.21</td>\n",
       "      <td>79.30</td>\n",
       "      <td>89.400</td>\n",
       "      <td>100.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VPmax (mbar)</th>\n",
       "      <td>70091.0</td>\n",
       "      <td>13.576576</td>\n",
       "      <td>7.739883</td>\n",
       "      <td>0.97</td>\n",
       "      <td>7.77</td>\n",
       "      <td>11.82</td>\n",
       "      <td>17.610</td>\n",
       "      <td>63.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VPact (mbar)</th>\n",
       "      <td>70091.0</td>\n",
       "      <td>9.533968</td>\n",
       "      <td>4.183658</td>\n",
       "      <td>0.81</td>\n",
       "      <td>6.22</td>\n",
       "      <td>8.86</td>\n",
       "      <td>12.360</td>\n",
       "      <td>28.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VPdef (mbar)</th>\n",
       "      <td>70091.0</td>\n",
       "      <td>4.042536</td>\n",
       "      <td>4.898549</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.87</td>\n",
       "      <td>2.19</td>\n",
       "      <td>5.300</td>\n",
       "      <td>46.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sh (g/kg)</th>\n",
       "      <td>70091.0</td>\n",
       "      <td>6.022560</td>\n",
       "      <td>2.655812</td>\n",
       "      <td>0.51</td>\n",
       "      <td>3.92</td>\n",
       "      <td>5.59</td>\n",
       "      <td>7.800</td>\n",
       "      <td>18.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>H2OC (mmol/mol)</th>\n",
       "      <td>70091.0</td>\n",
       "      <td>9.640437</td>\n",
       "      <td>4.234862</td>\n",
       "      <td>0.81</td>\n",
       "      <td>6.29</td>\n",
       "      <td>8.96</td>\n",
       "      <td>12.490</td>\n",
       "      <td>28.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rho (g/m**3)</th>\n",
       "      <td>70091.0</td>\n",
       "      <td>1216.061232</td>\n",
       "      <td>39.974263</td>\n",
       "      <td>1059.45</td>\n",
       "      <td>1187.47</td>\n",
       "      <td>1213.80</td>\n",
       "      <td>1242.765</td>\n",
       "      <td>1393.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>wv (m/s)</th>\n",
       "      <td>70091.0</td>\n",
       "      <td>1.702567</td>\n",
       "      <td>65.447512</td>\n",
       "      <td>-9999.00</td>\n",
       "      <td>0.99</td>\n",
       "      <td>1.76</td>\n",
       "      <td>2.860</td>\n",
       "      <td>14.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max. wv (m/s)</th>\n",
       "      <td>70091.0</td>\n",
       "      <td>2.963041</td>\n",
       "      <td>75.597657</td>\n",
       "      <td>-9999.00</td>\n",
       "      <td>1.76</td>\n",
       "      <td>2.98</td>\n",
       "      <td>4.740</td>\n",
       "      <td>23.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>wd (deg)</th>\n",
       "      <td>70091.0</td>\n",
       "      <td>174.789095</td>\n",
       "      <td>86.619431</td>\n",
       "      <td>0.00</td>\n",
       "      <td>125.30</td>\n",
       "      <td>198.10</td>\n",
       "      <td>234.000</td>\n",
       "      <td>360.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   count         mean        std      min      25%      50%  \\\n",
       "p (mbar)         70091.0   989.212842   8.358886   913.60   984.20   989.57   \n",
       "T (degC)         70091.0     9.450482   8.423384   -22.76     3.35     9.41   \n",
       "Tpot (K)         70091.0   283.493086   8.504424   250.85   277.44   283.46   \n",
       "Tdew (degC)      70091.0     4.956471   6.730081   -24.80     0.24     5.21   \n",
       "rh (%)           70091.0    76.009788  16.474920    13.88    65.21    79.30   \n",
       "VPmax (mbar)     70091.0    13.576576   7.739883     0.97     7.77    11.82   \n",
       "VPact (mbar)     70091.0     9.533968   4.183658     0.81     6.22     8.86   \n",
       "VPdef (mbar)     70091.0     4.042536   4.898549     0.00     0.87     2.19   \n",
       "sh (g/kg)        70091.0     6.022560   2.655812     0.51     3.92     5.59   \n",
       "H2OC (mmol/mol)  70091.0     9.640437   4.234862     0.81     6.29     8.96   \n",
       "rho (g/m**3)     70091.0  1216.061232  39.974263  1059.45  1187.47  1213.80   \n",
       "wv (m/s)         70091.0     1.702567  65.447512 -9999.00     0.99     1.76   \n",
       "max. wv (m/s)    70091.0     2.963041  75.597657 -9999.00     1.76     2.98   \n",
       "wd (deg)         70091.0   174.789095  86.619431     0.00   125.30   198.10   \n",
       "\n",
       "                      75%      max  \n",
       "p (mbar)          994.720  1015.29  \n",
       "T (degC)           15.480    37.28  \n",
       "Tpot (K)          289.530   311.21  \n",
       "Tdew (degC)        10.080    23.06  \n",
       "rh (%)             89.400   100.00  \n",
       "VPmax (mbar)       17.610    63.77  \n",
       "VPact (mbar)       12.360    28.25  \n",
       "VPdef (mbar)        5.300    46.01  \n",
       "sh (g/kg)           7.800    18.07  \n",
       "H2OC (mmol/mol)    12.490    28.74  \n",
       "rho (g/m**3)     1242.765  1393.54  \n",
       "wv (m/s)            2.860    14.01  \n",
       "max. wv (m/s)       4.740    23.50  \n",
       "wd (deg)          234.000   360.00  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe().transpose()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "TzOTnWOoWMGK"
   },
   "source": [
    "#### Wind velocity"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "i47LiW5DCVsP"
   },
   "source": [
    "One thing that should stand out is the `min` value of the wind velocity, `wv (m/s)` and `max. wv (m/s)` columns. This `-9999` is likely erroneous. There's a separate wind direction column, so the velocity should be `>=0`. Replace it with zeros:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-15T01:30:17.307985Z",
     "iopub.status.busy": "2020-10-15T01:30:17.307340Z",
     "iopub.status.idle": "2020-10-15T01:30:17.319639Z",
     "shell.execute_reply": "2020-10-15T01:30:17.319053Z"
    },
    "id": "qFOq0_80vF4d"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wv = df['wv (m/s)']\n",
    "bad_wv = wv == -9999.0\n",
    "wv[bad_wv] = 0.0\n",
    "\n",
    "max_wv = df['max. wv (m/s)']\n",
    "bad_max_wv = max_wv == -9999.0\n",
    "max_wv[bad_max_wv] = 0.0\n",
    "\n",
    "# The above inplace edits are reflected in the DataFrame\n",
    "df['wv (m/s)'].min()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "vtmu2IBPgPG8"
   },
   "source": [
    "### Feature engineering\n",
    "\n",
    "Before diving in to build a model it's important to understand your data, and be sure that you're passing the model appropriately formatted data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "FYyEaqiD6j4s"
   },
   "source": [
    "#### Wind\n",
    "The last column of the data, `wd (deg)`, gives the wind direction in units of degrees. Angles do not make good model inputs, 360° and 0° should be close to each other, and wrap around smoothly. Direction shouldn't matter if the wind is not blowing. \n",
    "\n",
    "Right now the distribution of wind data looks like this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-15T01:30:17.338749Z",
     "iopub.status.busy": "2020-10-15T01:30:17.338038Z",
     "iopub.status.idle": "2020-10-15T01:30:17.575839Z",
     "shell.execute_reply": "2020-10-15T01:30:17.576300Z"
    },
    "id": "YO7JGTcWQG2z"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Wind Velocity [m/s]')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist2d(df['wd (deg)'], df['wv (m/s)'], bins=(50, 50), vmax=400)\n",
    "plt.colorbar()\n",
    "plt.xlabel('Wind Direction [deg]')\n",
    "plt.ylabel('Wind Velocity [m/s]')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "yWnf5dwMU1_g"
   },
   "source": [
    "But this will be easier for the model to interpret if you convert the wind direction and velocity columns to a wind **vector**:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-15T01:30:17.582331Z",
     "iopub.status.busy": "2020-10-15T01:30:17.581585Z",
     "iopub.status.idle": "2020-10-15T01:30:17.607995Z",
     "shell.execute_reply": "2020-10-15T01:30:17.608529Z"
    },
    "id": "6GmSTHXw6lI1"
   },
   "outputs": [],
   "source": [
    "wv = df.pop('wv (m/s)')\n",
    "max_wv = df.pop('max. wv (m/s)')\n",
    "\n",
    "# Convert to radians.\n",
    "wd_rad = df.pop('wd (deg)')*np.pi / 180\n",
    "\n",
    "# Calculate the wind x and y components.\n",
    "df['Wx'] = wv*np.cos(wd_rad)\n",
    "df['Wy'] = wv*np.sin(wd_rad)\n",
    "\n",
    "# Calculate the max wind x and y components.\n",
    "df['max Wx'] = max_wv*np.cos(wd_rad)\n",
    "df['max Wy'] = max_wv*np.sin(wd_rad)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "7iI0zDoxWDyB"
   },
   "source": [
    "The distribution of wind vectors is much simpler for the model to correctly interpret."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-15T01:30:17.627369Z",
     "iopub.status.busy": "2020-10-15T01:30:17.625806Z",
     "iopub.status.idle": "2020-10-15T01:30:17.861215Z",
     "shell.execute_reply": "2020-10-15T01:30:17.861621Z"
    },
    "id": "bMgCG5o2SYKD"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-11.305513973134667, 8.24469928549079, -8.27438540335515, 7.7338312955467785)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist2d(df['Wx'], df['Wy'], bins=(50, 50), vmax=400)\n",
    "plt.colorbar()\n",
    "plt.xlabel('Wind X [m/s]')\n",
    "plt.ylabel('Wind Y [m/s]')\n",
    "ax = plt.gca()\n",
    "ax.axis('tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "_8im1ttOWlRB"
   },
   "source": [
    "#### Time"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "7YE21HKK40zQ"
   },
   "source": [
    "Similarly the `Date Time` column is very useful, but not in this string form. Start by converting it to seconds:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-15T01:30:17.870694Z",
     "iopub.status.busy": "2020-10-15T01:30:17.865276Z",
     "iopub.status.idle": "2020-10-15T01:30:18.280230Z",
     "shell.execute_reply": "2020-10-15T01:30:18.280738Z"
    },
    "id": "LIFf-VjMfnh3"
   },
   "outputs": [],
   "source": [
    "timestamp_s = date_time.map(datetime.datetime.timestamp)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "EC_pnM1D5Sgc"
   },
   "source": [
    "Similar to the wind direction the time in seconds is not a useful model input. Being weather data it has clear daily and yearly periodicity. There are many ways you could deal with periodicity.\n",
    "\n",
    "A simple approach to convert it to a usable signal is to use `sin` and `cos` to convert the time to clear \"Time of day\" and \"Time of year\" signals:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-15T01:30:18.286676Z",
     "iopub.status.busy": "2020-10-15T01:30:18.285958Z",
     "iopub.status.idle": "2020-10-15T01:30:18.299207Z",
     "shell.execute_reply": "2020-10-15T01:30:18.298681Z"
    },
    "id": "MBfX6CDwax73"
   },
   "outputs": [],
   "source": [
    "day = 24*60*60\n",
    "year = (365.2425)*day\n",
    "\n",
    "df['Day sin'] = np.sin(timestamp_s * (2 * np.pi / day))\n",
    "df['Day cos'] = np.cos(timestamp_s * (2 * np.pi / day))\n",
    "df['Year sin'] = np.sin(timestamp_s * (2 * np.pi / year))\n",
    "df['Year cos'] = np.cos(timestamp_s * (2 * np.pi / year))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-15T01:30:18.316732Z",
     "iopub.status.busy": "2020-10-15T01:30:18.303373Z",
     "iopub.status.idle": "2020-10-15T01:30:18.438651Z",
     "shell.execute_reply": "2020-10-15T01:30:18.439068Z"
    },
    "id": "mXBbTJZfuuTC"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Time of day signal')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(np.array(df['Day sin'])[:25])\n",
    "plt.plot(np.array(df['Day cos'])[:25])\n",
    "plt.xlabel('Time [h]')\n",
    "plt.title('Time of day signal')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "HiurzTGQgf_D"
   },
   "source": [
    "This gives the model access to the most important frequency features. In this case you knew ahead of time which frequencies were important. \n",
    "\n",
    "If you didn't know, you can determine which frequencies are important using an `fft`. To check our assumptions, here is the `tf.signal.rfft` of the temperature over time. Note the obvious peaks at frequencies near `1/year` and `1/day`: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-15T01:30:19.829340Z",
     "iopub.status.busy": "2020-10-15T01:30:19.828504Z",
     "iopub.status.idle": "2020-10-15T01:30:20.732409Z",
     "shell.execute_reply": "2020-10-15T01:30:20.732836Z"
    },
    "id": "EN4U1fcMiTYs"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fft = tf.signal.rfft(df['T (degC)'])\n",
    "f_per_dataset = np.arange(0, len(fft))\n",
    "\n",
    "n_samples_h = len(df['T (degC)'])\n",
    "hours_per_year = 24*365.2524\n",
    "years_per_dataset = n_samples_h/(hours_per_year)\n",
    "\n",
    "f_per_year = f_per_dataset/years_per_dataset\n",
    "plt.step(f_per_year, np.abs(fft))\n",
    "plt.xscale('log')\n",
    "plt.ylim(0, 400000)\n",
    "plt.xlim([0.1, max(plt.xlim())])\n",
    "plt.xticks([1, 365.2524], labels=['1/Year', '1/day'])\n",
    "_ = plt.xlabel('Frequency (log scale)')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "2rbL8bSGDHy3"
   },
   "source": [
    "### Split the data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "qoFJZmXBaxCc"
   },
   "source": [
    "We'll use a `(70%, 20%, 10%)` split for the training, validation, and test sets. Note the data is **not** being randomly shuffled before splitting. This is for two reasons.\n",
    "\n",
    "1. It ensures that chopping the data into windows of consecutive samples is still possible.\n",
    "2. It ensures that the validation/test results are more realistic, being evaluated on data collected after the model was trained."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-15T01:30:20.738956Z",
     "iopub.status.busy": "2020-10-15T01:30:20.738221Z",
     "iopub.status.idle": "2020-10-15T01:30:20.739768Z",
     "shell.execute_reply": "2020-10-15T01:30:20.740301Z"
    },
    "id": "ia-MPAHxbInX"
   },
   "outputs": [],
   "source": [
    "column_indices = {name: i for i, name in enumerate(df.columns)}\n",
    "\n",
    "n = len(df)\n",
    "train_df = df[0:int(n*0.7)]\n",
    "val_df = df[int(n*0.7):int(n*0.9)]\n",
    "test_df = df[int(n*0.9):]\n",
    "\n",
    "num_features = df.shape[1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "-eFckdUUHWmT"
   },
   "source": [
    "### Normalize the data\n",
    "\n",
    "It is important to scale features before training a neural network. Normalization is a common way of doing this scaling. Subtract the mean and divide by the standard deviation of each feature."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "mxbIic5TMlxx"
   },
   "source": [
    "The mean and standard deviation should only be computed using the training data so that the models have no access to the values in the validation and test sets.\n",
    "\n",
    "It's also arguable that the model shouldn't have access to future values in the training set when training, and that this normalization should be done using moving averages. That's not the focus of this tutorial, and the validation and test sets ensure that you get (somewhat) honest metrics. So in the interest of simplicity this tutorial uses a simple average."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-15T01:30:20.745072Z",
     "iopub.status.busy": "2020-10-15T01:30:20.744146Z",
     "iopub.status.idle": "2020-10-15T01:30:20.778748Z",
     "shell.execute_reply": "2020-10-15T01:30:20.779214Z"
    },
    "id": "Eji6njXvHusN"
   },
   "outputs": [],
   "source": [
    "train_mean = train_df.mean()\n",
    "train_std = train_df.std()\n",
    "\n",
    "train_df = (train_df - train_mean) / train_std\n",
    "val_df = (val_df - train_mean) / train_std\n",
    "test_df = (test_df - train_mean) / train_std"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "G6ufs8kk9JQw"
   },
   "source": [
    "Now peek at the distribution of the features. Some features do have long tails, but there are no obvious errors like the `-9999` wind velocity value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-15T01:30:20.784822Z",
     "iopub.status.busy": "2020-10-15T01:30:20.784124Z",
     "iopub.status.idle": "2020-10-15T01:30:26.347075Z",
     "shell.execute_reply": "2020-10-15T01:30:26.347547Z"
    },
    "id": "T0UYEnkwm8Fe"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_std = (df - train_mean) / train_std\n",
    "df_std = df_std.melt(var_name='Column', value_name='Normalized')\n",
    "plt.figure(figsize=(12, 6))\n",
    "ax = sns.violinplot(x='Column', y='Normalized', data=df_std)\n",
    "_ = ax.set_xticklabels(df.keys(), rotation=90)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ZBBmdxZ2HgfJ"
   },
   "source": [
    "## Data windowing\n",
    "\n",
    "The models in this tutorial will make a set of predictions based on a window of consecutive samples from the data. \n",
    "\n",
    "The main features of the input windows are:\n",
    "\n",
    "* The width (number of time steps) of the input and label windows\n",
    "* The time offset between them.\n",
    "* Which features are used as inputs, labels, or both. \n",
    "\n",
    "This tutorial builds a variety of models (including Linear, DNN, CNN and RNN models), and uses them for both:\n",
    "\n",
    "* *Single-output*, and *multi-output* predictions.\n",
    "* *Single-time-step* and *multi-time-step* predictions.\n",
    "\n",
    "This section focuses on implementing the data windowing so that it can be reused for all of those models.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "YAhGUVx1jtOy"
   },
   "source": [
    "Depending on the task and type of model you may want to generate a variety of data windows. Here are some examples:\n",
    "\n",
    "1. For example, to make a single prediction 24h into the future, given 24h of history you might define a window like this:\n",
    "\n",
    "  ![One prediction 24h into the future.](images/raw_window_24h.png)\n",
    "\n",
    "2. A model that makes a prediction 1h into the future, given 6h of history would need a window like this:\n",
    "\n",
    "  ![One prediction 1h into the future.](images/raw_window_1h.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "sa2BbfNZt8wy"
   },
   "source": [
    "The rest of this section defines a `WindowGenerator` class. This class can:\n",
    "\n",
    "1. Handle the indexes and offsets as shown in the diagrams above.\n",
    "1. Split windows of features into a `(features, labels)` pairs.\n",
    "2. Plot the content of the resulting windows.\n",
    "3. Efficiently generate batches of these windows from the training, evaluation, and test data, using `tf.data.Dataset`s."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "rfx3jGjyziUF"
   },
   "source": [
    "### 1. Indexes and offsets\n",
    "\n",
    "Start by creating the `WindowGenerator` class. The `__init__` method includes all the necessary logic for the input and label indices.\n",
    "\n",
    "It also takes the train, eval, and test dataframes as input. These will be converted to `tf.data.Dataset`s of windows later."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-15T01:30:26.356886Z",
     "iopub.status.busy": "2020-10-15T01:30:26.356210Z",
     "iopub.status.idle": "2020-10-15T01:30:26.358034Z",
     "shell.execute_reply": "2020-10-15T01:30:26.358482Z"
    },
    "id": "Kem30j8QHxyW"
   },
   "outputs": [],
   "source": [
    "class WindowGenerator():\n",
    "  def __init__(self, input_width, label_width, shift,\n",
    "               train_df=train_df, val_df=val_df, test_df=test_df,\n",
    "               label_columns=None):\n",
    "    # Store the raw data.\n",
    "    self.train_df = train_df\n",
    "    self.val_df = val_df\n",
    "    self.test_df = test_df\n",
    "\n",
    "    # Work out the label column indices.\n",
    "    self.label_columns = label_columns\n",
    "    if label_columns is not None:\n",
    "      self.label_columns_indices = {name: i for i, name in\n",
    "                                    enumerate(label_columns)}\n",
    "    self.column_indices = {name: i for i, name in\n",
    "                           enumerate(train_df.columns)}\n",
    "\n",
    "    # Work out the window parameters.\n",
    "    self.input_width = input_width\n",
    "    self.label_width = label_width\n",
    "    self.shift = shift\n",
    "\n",
    "    self.total_window_size = input_width + shift\n",
    "\n",
    "    self.input_slice = slice(0, input_width)\n",
    "    self.input_indices = np.arange(self.total_window_size)[self.input_slice]\n",
    "\n",
    "    self.label_start = self.total_window_size - self.label_width\n",
    "    self.labels_slice = slice(self.label_start, None)\n",
    "    self.label_indices = np.arange(self.total_window_size)[self.labels_slice]\n",
    "\n",
    "  def __repr__(self):\n",
    "    return '\\n'.join([\n",
    "        f'Total window size: {self.total_window_size}',\n",
    "        f'Input indices: {self.input_indices}',\n",
    "        f'Label indices: {self.label_indices}',\n",
    "        f'Label column name(s): {self.label_columns}'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "yVJgblsYzL1g"
   },
   "source": [
    "Here is code to create the 2 windows shown in the diagrams at the start of this section:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-15T01:30:26.363195Z",
     "iopub.status.busy": "2020-10-15T01:30:26.362541Z",
     "iopub.status.idle": "2020-10-15T01:30:26.365503Z",
     "shell.execute_reply": "2020-10-15T01:30:26.365006Z"
    },
    "id": "IsM5kRkz0UwK"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Total window size: 48\n",
       "Input indices: [ 0  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22 23]\n",
       "Label indices: [47]\n",
       "Label column name(s): ['T (degC)']"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "w1 = WindowGenerator(input_width=24, label_width=1, shift=24,\n",
    "                     label_columns=['T (degC)'])\n",
    "w1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-15T01:30:26.369811Z",
     "iopub.status.busy": "2020-10-15T01:30:26.369126Z",
     "iopub.status.idle": "2020-10-15T01:30:26.371743Z",
     "shell.execute_reply": "2020-10-15T01:30:26.372166Z"
    },
    "id": "viwKsYeAKFUn"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Total window size: 7\n",
       "Input indices: [0 1 2 3 4 5]\n",
       "Label indices: [6]\n",
       "Label column name(s): ['T (degC)']"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "w2 = WindowGenerator(input_width=6, label_width=1, shift=1,\n",
    "                     label_columns=['T (degC)'])\n",
    "w2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "kJaUyTWQJd-L"
   },
   "source": [
    "### 2. Split\n",
    "Given a list consecutive inputs, the `split_window` method will convert them to a window of inputs and a window of labels.\n",
    "\n",
    "The example `w2`, above, will be split like this:\n",
    "\n",
    "![The initial window is all consecuitive samples, this splits it into an (inputs, labels) pairs](images/split_window.png)\n",
    "\n",
    "This diagram doesn't show the `features` axis of the data, but this `split_window` function also handles the `label_columns` so it can be used for both the single output and multi-output examples."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-15T01:30:26.378101Z",
     "iopub.status.busy": "2020-10-15T01:30:26.377445Z",
     "iopub.status.idle": "2020-10-15T01:30:26.379470Z",
     "shell.execute_reply": "2020-10-15T01:30:26.379873Z"
    },
    "id": "W4KbxfzqkXPW"
   },
   "outputs": [],
   "source": [
    "def split_window(self, features):\n",
    "  inputs = features[:, self.input_slice, :]\n",
    "  labels = features[:, self.labels_slice, :]\n",
    "  if self.label_columns is not None:\n",
    "    labels = tf.stack(\n",
    "        [labels[:, :, self.column_indices[name]] for name in self.label_columns],\n",
    "        axis=-1)\n",
    "\n",
    "  # Slicing doesn't preserve static shape information, so set the shapes\n",
    "  # manually. This way the `tf.data.Datasets` are easier to inspect.\n",
    "  inputs.set_shape([None, self.input_width, None])\n",
    "  labels.set_shape([None, self.label_width, None])\n",
    "\n",
    "  return inputs, labels\n",
    "\n",
    "WindowGenerator.split_window = split_window"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "G6U6VtVuM15s"
   },
   "source": [
    "Try it out:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-15T01:30:26.386073Z",
     "iopub.status.busy": "2020-10-15T01:30:26.385429Z",
     "iopub.status.idle": "2020-10-15T01:30:26.390047Z",
     "shell.execute_reply": "2020-10-15T01:30:26.389532Z"
    },
    "id": "YeCWbq6KLmL7"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "All shapes are: (batch, time, features)\n",
      "Window shape: (3, 7, 19)\n",
      "Inputs shape: (3, 6, 19)\n",
      "labels shape: (3, 1, 1)\n"
     ]
    }
   ],
   "source": [
    "# Stack three slices, the length of the total window:\n",
    "example_window = tf.stack([np.array(train_df[:w2.total_window_size]),\n",
    "                           np.array(train_df[100:100+w2.total_window_size]),\n",
    "                           np.array(train_df[200:200+w2.total_window_size])])\n",
    "\n",
    "\n",
    "example_inputs, example_labels = w2.split_window(example_window)\n",
    "\n",
    "print('All shapes are: (batch, time, features)')\n",
    "print(f'Window shape: {example_window.shape}')\n",
    "print(f'Inputs shape: {example_inputs.shape}')\n",
    "print(f'labels shape: {example_labels.shape}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "xtMk1ffk2Mmd"
   },
   "source": [
    "Typically data in TensorFlow is packed into arrays where the outermost index is across examples (the \"batch\" dimension). The middle indices are the \"time\" or \"space\" (width, height) dimension(s). The innermost indices are the features.\n",
    "\n",
    "The code above took a batch of 3, 7-timestep windows, with 19 features at each time step. It split them into a batch of 6-timestep, 19 feature inputs, and a 1-timestep 1-feature label. The label only has one feature because the `WindowGenerator` was initialized with `label_columns=['T (degC)']`. Initially this tutorial will build models that predict single output labels."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "tFZukGXrJoGo"
   },
   "source": [
    "### 3. Plot\n",
    "\n",
    "Here is a plot method that allows a simple visualization of the split window:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-15T01:30:26.393945Z",
     "iopub.status.busy": "2020-10-15T01:30:26.393333Z",
     "iopub.status.idle": "2020-10-15T01:30:26.395808Z",
     "shell.execute_reply": "2020-10-15T01:30:26.395192Z"
    },
    "id": "fmgd1qkYUWT7"
   },
   "outputs": [],
   "source": [
    "w2.example = example_inputs, example_labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-15T01:30:26.404444Z",
     "iopub.status.busy": "2020-10-15T01:30:26.403753Z",
     "iopub.status.idle": "2020-10-15T01:30:26.405779Z",
     "shell.execute_reply": "2020-10-15T01:30:26.406200Z"
    },
    "id": "jIrYccI-Hm3B"
   },
   "outputs": [],
   "source": [
    "def plot(self, model=None, plot_col='T (degC)', max_subplots=3):\n",
    "  inputs, labels = self.example\n",
    "  plt.figure(figsize=(12, 8))\n",
    "  plot_col_index = self.column_indices[plot_col]\n",
    "  max_n = min(max_subplots, len(inputs))\n",
    "  for n in range(max_n):\n",
    "    plt.subplot(3, 1, n+1)\n",
    "    plt.ylabel(f'{plot_col} [normed]')\n",
    "    plt.plot(self.input_indices, inputs[n, :, plot_col_index],\n",
    "             label='Inputs', marker='.', zorder=-10)\n",
    "\n",
    "    if self.label_columns:\n",
    "      label_col_index = self.label_columns_indices.get(plot_col, None)\n",
    "    else:\n",
    "      label_col_index = plot_col_index\n",
    "\n",
    "    if label_col_index is None:\n",
    "      continue\n",
    "\n",
    "    plt.scatter(self.label_indices, labels[n, :, label_col_index],\n",
    "                edgecolors='k', label='Labels', c='#2ca02c', s=64)\n",
    "    if model is not None:\n",
    "      predictions = model(inputs)\n",
    "      plt.scatter(self.label_indices, predictions[n, :, label_col_index],\n",
    "                  marker='X', edgecolors='k', label='Predictions',\n",
    "                  c='#ff7f0e', s=64)\n",
    "\n",
    "    if n == 0:\n",
    "      plt.legend()\n",
    "\n",
    "  plt.xlabel('Time [h]')\n",
    "\n",
    "WindowGenerator.plot = plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot24(self, model=None, plot_col='T (degC)', max_subplots=3):\n",
    "  inputs, labels = self.example\n",
    "  plt.figure(figsize=(12, 8))\n",
    "  plot_col_index = self.column_indices[plot_col]\n",
    "  max_n = min(max_subplots, len(inputs))\n",
    "  for n in range(max_n):\n",
    "    plt.subplot(3, 1, n+1)\n",
    "    plt.ylabel(f'{plot_col} [normed]')\n",
    "    plt.plot(self.input_indices, inputs[n, :, plot_col_index],\n",
    "             label='Inputs', marker='.', zorder=-10)\n",
    "\n",
    "    if self.label_columns:\n",
    "      label_col_index = self.label_columns_indices.get(plot_col, None)\n",
    "    else:\n",
    "      label_col_index = plot_col_index\n",
    "\n",
    "    if label_col_index is None:\n",
    "      continue\n",
    "\n",
    "    plt.scatter(self.label_indices, labels[n, :, label_col_index],\n",
    "                edgecolors='k', label='Labels', c='#2ca02c', s=64)\n",
    "    if model is not None:\n",
    "      predictions = model(inputs)\n",
    "      plt.scatter(self.label_indices, predictions[n, 0, label_col_index],\n",
    "                  marker='X', edgecolors='k', label='Predictions',\n",
    "                  c='#ff7f0e', s=64)\n",
    "\n",
    "    if n == 0:\n",
    "      plt.legend()\n",
    "\n",
    "  plt.xlabel('Time [h]')\n",
    "\n",
    "WindowGenerator.plot24 = plot24"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "HXvctEuK68vX"
   },
   "source": [
    "This plot aligns inputs, labels, and (later) predictions based on the time that the item refers to:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-15T01:30:26.423349Z",
     "iopub.status.busy": "2020-10-15T01:30:26.421706Z",
     "iopub.status.idle": "2020-10-15T01:30:26.758388Z",
     "shell.execute_reply": "2020-10-15T01:30:26.758871Z"
    },
    "id": "XjTqUnglOOni"
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 864x576 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "w2.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "UqiqcPOldPG6"
   },
   "source": [
    "You can plot the other columns, but the example window `w2` configuration only has labels for the `T (degC)` column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-15T01:30:26.774940Z",
     "iopub.status.busy": "2020-10-15T01:30:26.763156Z",
     "iopub.status.idle": "2020-10-15T01:30:27.038710Z",
     "shell.execute_reply": "2020-10-15T01:30:27.039200Z"
    },
    "id": "EBRe4wnlfCH8"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "w2.plot(plot_col='p (mbar)')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "xCvD-UaUzYMw"
   },
   "source": [
    "### 4. Create `tf.data.Dataset`s"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "kLO3SFR9Osdf"
   },
   "source": [
    "Finally this `make_dataset` method will take a time series `DataFrame` and convert it to a `tf.data.Dataset` of `(input_window, label_window)` pairs using the `preprocessing.timeseries_dataset_from_array` function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-15T01:30:27.044864Z",
     "iopub.status.busy": "2020-10-15T01:30:27.044204Z",
     "iopub.status.idle": "2020-10-15T01:30:27.045946Z",
     "shell.execute_reply": "2020-10-15T01:30:27.046386Z"
    },
    "id": "35qoSQeRVfJg"
   },
   "outputs": [],
   "source": [
    "def make_dataset(self, data):\n",
    "  data = np.array(data, dtype=np.float32)\n",
    "  ds = tf.keras.preprocessing.timeseries_dataset_from_array(\n",
    "      data=data,\n",
    "      targets=None,\n",
    "      sequence_length=self.total_window_size,\n",
    "      sequence_stride=1,\n",
    "      shuffle=True,\n",
    "      batch_size=32,)\n",
    "\n",
    "  ds = ds.map(self.split_window)\n",
    "\n",
    "  return ds\n",
    "\n",
    "WindowGenerator.make_dataset = make_dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "LvsxQwJaCift"
   },
   "source": [
    "The `WindowGenerator` object holds training, validation and test data. Add properties for accessing them as `tf.data.Datasets` using the above `make_dataset` method. Also add a standard example batch for easy access and plotting:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-15T01:30:27.053241Z",
     "iopub.status.busy": "2020-10-15T01:30:27.052497Z",
     "iopub.status.idle": "2020-10-15T01:30:27.054175Z",
     "shell.execute_reply": "2020-10-15T01:30:27.054701Z"
    },
    "id": "2jZ2KkqGCfzu"
   },
   "outputs": [],
   "source": [
    "@property\n",
    "def train(self):\n",
    "  return self.make_dataset(self.train_df)\n",
    "\n",
    "@property\n",
    "def val(self):\n",
    "  return self.make_dataset(self.val_df)\n",
    "\n",
    "@property\n",
    "def test(self):\n",
    "  return self.make_dataset(self.test_df)\n",
    "\n",
    "@property\n",
    "def example(self):\n",
    "  \"\"\"Get and cache an example batch of `inputs, labels` for plotting.\"\"\"\n",
    "  result = getattr(self, '_example', None)\n",
    "  if result is None:\n",
    "    # No example batch was found, so get one from the `.train` dataset\n",
    "    result = next(iter(self.train))\n",
    "    # And cache it for next time\n",
    "    self._example = result\n",
    "  return result\n",
    "\n",
    "WindowGenerator.train = train\n",
    "WindowGenerator.val = val\n",
    "WindowGenerator.test = test\n",
    "WindowGenerator.example = example"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "fF_Vj6Iw3Y2w"
   },
   "source": [
    "Now the `WindowGenerator` object gives you access to the `tf.data.Dataset` objects, so you can easily iterate over the data.\n",
    "\n",
    "The `Dataset.element_spec` property tells you the structure, `dtypes` and shapes of the dataset elements."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-15T01:30:27.058395Z",
     "iopub.status.busy": "2020-10-15T01:30:27.057676Z",
     "iopub.status.idle": "2020-10-15T01:30:27.192810Z",
     "shell.execute_reply": "2020-10-15T01:30:27.192285Z"
    },
    "id": "daJ0-U383YVs"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(TensorSpec(shape=(None, 6, 19), dtype=tf.float32, name=None),\n",
       " TensorSpec(shape=(None, 1, 1), dtype=tf.float32, name=None))"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Each element is an (inputs, label) pair\n",
    "w2.train.element_spec"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "XKTx3_Z7ua-n"
   },
   "source": [
    "Iterating over a `Dataset` yields concrete batches:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-15T01:30:27.197112Z",
     "iopub.status.busy": "2020-10-15T01:30:27.196421Z",
     "iopub.status.idle": "2020-10-15T01:30:27.272087Z",
     "shell.execute_reply": "2020-10-15T01:30:27.271536Z"
    },
    "id": "6gtKXEgf4Iml"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Inputs shape (batch, time, features): (32, 6, 19)\n",
      "Labels shape (batch, time, features): (32, 1, 1)\n"
     ]
    }
   ],
   "source": [
    "for example_inputs, example_labels in w2.train.take(1):\n",
    "  print(f'Inputs shape (batch, time, features): {example_inputs.shape}')\n",
    "  print(f'Labels shape (batch, time, features): {example_labels.shape}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ----------------------- The LAB -------------------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## C. 3 models to predict 24 hours forward"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Model 1 - Direct prediction (the 24th value forward)<br>\n",
    "Model 2 - One-shot prediction of all 24 values forward at once<br>\n",
    "Model 3 - One time predictions 24 times (All 24 values forward)<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### C. Model 1\n",
    "We will use Model 2 from part B, and adjust for prediction 24 hours forward"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Nicely formatted time string\n",
    "import time\n",
    "def hms_string(sec_elapsed):\n",
    "    h = int(sec_elapsed / (60 * 60))\n",
    "    m = int((sec_elapsed % (60 * 60)) / 60)\n",
    "    s = sec_elapsed % 60\n",
    "    return f\"{h}:{m:>02}:{s:>05.2f}\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "import time\n",
    "from tensorflow.keras.models import Sequential\n",
    "from kerastuner.tuners import BayesianOptimization\n",
    "from tensorflow.keras.layers import Dense, Dropout, Flatten\n",
    "import tensorflow.keras\n",
    "import keras\n",
    "from tensorflow.keras.callbacks import EarlyStopping\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "val_performance = {}\n",
    "performance = {}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Total window size: 48\n",
       "Input indices: [ 0  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22 23]\n",
       "Label indices: [47]\n",
       "Label column name(s): ['T (degC)']"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Make forcast window with 24 (input_width + label_width) values to base the one step (shift) 24 forward predicion on.\n",
    "\n",
    "tewntyfour_step_window = WindowGenerator(\n",
    "    input_width=24, label_width=1, shift=24,\n",
    "    label_columns=['T (degC)'])\n",
    "tewntyfour_step_window"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Inputs shape (batch, time, features): (32, 24, 19)\n",
      "Labels shape (batch, time, features): (32, 1, 1)\n"
     ]
    }
   ],
   "source": [
    "for example_inputs, example_labels in tewntyfour_step_window.train.take(1):\n",
    "  print(f'Inputs shape (batch, time, features): {example_inputs.shape}')\n",
    "  print(f'Labels shape (batch, time, features): {example_labels.shape}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "units = 32\n",
    "\n",
    "modelC1 = tf.keras.models.Sequential([\n",
    "    tf.keras.layers.LSTM(units, \n",
    "        return_sequences=True),\n",
    "    tf.keras.layers.Dense(units=1)    \n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "model C1\n",
      "Input shape: (32, 24, 19)\n",
      "Output shape: (32, 24, 1)\n"
     ]
    }
   ],
   "source": [
    "print('')\n",
    "print('model C1')\n",
    "\n",
    "print('Input shape:', tewntyfour_step_window.example[0].shape)\n",
    "print('Output shape:', modelC1(tewntyfour_step_window.example[0]).shape)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "# compile and then do a fit of one model. It is easier to have it in the same method as we are using \n",
    "# the window function to make the training and validation data\n",
    "\n",
    "MAX_EPOCHS = 20\n",
    "\n",
    "def compile_and_fit(model, window, patience=2):\n",
    "  early_stopping = tf.keras.callbacks.EarlyStopping(monitor='val_loss',\n",
    "                                                    patience=patience,\n",
    "                                                    mode='min')\n",
    "\n",
    "  model.compile(loss=tf.losses.MeanSquaredError(),\n",
    "                optimizer=tf.optimizers.Adam(),\n",
    "                metrics=[tf.metrics.MeanAbsoluteError()])\n",
    "\n",
    "  history = model.fit(window.train, epochs=MAX_EPOCHS,\n",
    "                      validation_data=window.val,\n",
    "                        verbose=2,\n",
    "                      callbacks=[early_stopping])\n",
    "  return history"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n",
      "1532/1532 - 9s - loss: 0.1860 - mean_absolute_error: 0.3328 - val_loss: 0.1612 - val_mean_absolute_error: 0.3149\n",
      "Epoch 2/20\n",
      "1532/1532 - 8s - loss: 0.1513 - mean_absolute_error: 0.3036 - val_loss: 0.1576 - val_mean_absolute_error: 0.3115\n",
      "Epoch 3/20\n",
      "1532/1532 - 8s - loss: 0.1451 - mean_absolute_error: 0.2976 - val_loss: 0.1582 - val_mean_absolute_error: 0.3138\n",
      "Epoch 4/20\n",
      "1532/1532 - 8s - loss: 0.1413 - mean_absolute_error: 0.2931 - val_loss: 0.1552 - val_mean_absolute_error: 0.3105\n",
      "Epoch 5/20\n",
      "1532/1532 - 8s - loss: 0.1379 - mean_absolute_error: 0.2894 - val_loss: 0.1579 - val_mean_absolute_error: 0.3141\n",
      "Epoch 6/20\n",
      "1532/1532 - 8s - loss: 0.1353 - mean_absolute_error: 0.2863 - val_loss: 0.1567 - val_mean_absolute_error: 0.3117\n",
      "Elapsed time: 0:00:52.49\n"
     ]
    }
   ],
   "source": [
    "start_time = time.time()\n",
    "\n",
    "historyC1 = compile_and_fit(modelC1, tewntyfour_step_window)\n",
    "\n",
    "elapsed_time = time.time() - start_time\n",
    "print(\"Elapsed time: {}\".format(hms_string(elapsed_time)))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "pd.DataFrame(historyC1.history).plot(figsize=(8,5))\n",
    "plt.grid(True)\n",
    "plt.gca().set_ylim(0,1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "437/437 [==============================] - ETA: 0s - loss: 0.1949 - mean_absolute_error: 0.341 - ETA: 0s - loss: 0.1721 - mean_absolute_error: 0.330 - ETA: 0s - loss: 0.1613 - mean_absolute_error: 0.315 - ETA: 0s - loss: 0.1584 - mean_absolute_error: 0.311 - ETA: 0s - loss: 0.1594 - mean_absolute_error: 0.313 - ETA: 0s - loss: 0.1606 - mean_absolute_error: 0.314 - ETA: 0s - loss: 0.1606 - mean_absolute_error: 0.314 - ETA: 0s - loss: 0.1605 - mean_absolute_error: 0.315 - ETA: 0s - loss: 0.1607 - mean_absolute_error: 0.315 - ETA: 0s - loss: 0.1605 - mean_absolute_error: 0.315 - ETA: 0s - loss: 0.1592 - mean_absolute_error: 0.313 - ETA: 0s - loss: 0.1585 - mean_absolute_error: 0.313 - ETA: 0s - loss: 0.1577 - mean_absolute_error: 0.312 - ETA: 0s - loss: 0.1575 - mean_absolute_error: 0.312 - ETA: 0s - loss: 0.1572 - mean_absolute_error: 0.312 - ETA: 0s - loss: 0.1566 - mean_absolute_error: 0.311 - ETA: 0s - loss: 0.1566 - mean_absolute_error: 0.311 - ETA: 0s - loss: 0.1568 - mean_absolute_error: 0.311 - 1s 2ms/step - loss: 0.1567 - mean_absolute_error: 0.3117\n"
     ]
    }
   ],
   "source": [
    "IPython.display.clear_output()\n",
    "val_performance['24 step'] = modelC1.evaluate(tewntyfour_step_window.val)\n",
    "performance['24 step'] = modelC1.evaluate(tewntyfour_step_window.test, verbose=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "24 step\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print('24 step')\n",
    "tewntyfour_step_window.plot24(modelC1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "24 step : 0.2912\n"
     ]
    }
   ],
   "source": [
    "for name, value in performance.items():\n",
    "  print(f'{name:8s}: {value[1]:0.4f}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### Comment: \n",
    "\n",
    "Looking at the MAE in models from part B (see the old file):<br>\n",
    "LSTM    : 0.0518<br>\n",
    "LSTM model 2: 0.0534<br>\n",
    "LSTM model 3: 0.0532<br>\n",
    "LSTM model 4: 0.0533<br>\n",
    "LSTM model 5: 0.0542<br>\n",
    "LSTM model 6: 0.0750<br>\n",
    "<br>\n",
    "The MAE is much higher (0.29) in this model where predictions are done 24 steps forward, than the models where predictions 1 step forwards. So it is more difficult to do the predictions further away in the future. We can also look at the graphs and observe that the predictions are not close to the actual values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Copyright"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This file is based on a tutorial (link in the top of the file), with the following Apache License"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Copyright 2019 The TensorFlow Authors.\n",
    "\n",
    "Licensed under the Apache License, Version 2.0 (the \"License\");\n",
    "you may not use this file except in compliance with the License.\n",
    "You may obtain a copy of the License at\n",
    "\n",
    "    http://www.apache.org/licenses/LICENSE-2.0\n",
    "\n",
    "Unless required by applicable law or agreed to in writing, software\n",
    "distributed under the License is distributed on an \"AS IS\" BASIS,\n",
    "WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
    "See the License for the specific language governing permissions and\n",
    "limitations under the License."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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